Deep Learning Realm for Geophysics: Seismic Acquisition, Processing, Interpretation, and Inversion

Applying deep-learning models to geophysical applications has attracted special attentions during the past a couple of years. There are several papers published in this domain involving with different topics primarily for synthetic seismic data. However, it is extremely hard to find documents with detailed illustrations of what kind of best practices should researchers follow regarding how to design appropriate deep-learning models to effectively tackle problems relevant to the field seismic. This paper serves as a summary to demonstrate successful stories and share with extensive experiences we have gained during past several years in the process of designing and deploying of deep-learning models to for the geophysical projects. Four different disciplines are discussed individually with seismic acquisition, processing, interpretation, and inversion. Finally, special attentions about designing an effective deep-neural-networks especially for geophysics are discussed.